from sklearn_benchmarks.reporting.hp_match import HPMatchReporting
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)

scikit-learn vs. scikit-learn-intelex (Intel® oneAPI)

reporting = HPMatchReporting(against_lib="sklearnex", config="config.yml")
reporting.make_report()

We assume here there is a perfect match between the hyperparameters of both librairies. For a given set of parameters and a given dataset, we compute the speedup time scikit-learn / time sklearnex. For instance, a speedup of 2 means that sklearnex is twice as fast as scikit-learn for a given set of parameters and a given dataset.

KNeighborsClassifier_brute_force

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: algorithm=brute.

WARNING! Mismatch between validation scores for 14 predictions (58.33%). See below.

	estimator	function	n_samples_train	n_samples	n_features	accuracy_score_sklearn	accuracy_score_sklearnex	diff_accuracy_scores
1	KNeighborsClassifier_brute_force	predict	100000	1000	100	0.663	0.687	0.024
4	KNeighborsClassifier_brute_force	predict	100000	1000	100	0.757	0.742	0.015
7	KNeighborsClassifier_brute_force	predict	100000	1000	100	0.882	0.875	0.007
8	KNeighborsClassifier_brute_force	predict	100000	1	100	1.000	0.000	1.000
10	KNeighborsClassifier_brute_force	predict	100000	1000	100	0.882	0.875	0.007
11	KNeighborsClassifier_brute_force	predict	100000	1	100	1.000	0.000	1.000
13	KNeighborsClassifier_brute_force	predict	100000	1000	100	0.757	0.742	0.015
16	KNeighborsClassifier_brute_force	predict	100000	1000	100	0.663	0.687	0.024
19	KNeighborsClassifier_brute_force	predict	100000	1000	2	0.896	0.967	0.071
22	KNeighborsClassifier_brute_force	predict	100000	1000	2	0.922	0.974	0.052
25	KNeighborsClassifier_brute_force	predict	100000	1000	2	0.929	0.975	0.046
28	KNeighborsClassifier_brute_force	predict	100000	1000	2	0.929	0.975	0.046
31	KNeighborsClassifier_brute_force	predict	100000	1000	2	0.922	0.974	0.052
34	KNeighborsClassifier_brute_force	predict	100000	1000	2	0.896	0.967	0.071

Raw results

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	n_jobs	n_neighbors	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.016	0.001	4.883	0.0	-1	1	0.069	0.005	0.237	0.238	See	See
3	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.016	0.000	4.937	0.0	-1	5	0.069	0.001	0.235	0.235	See	See
6	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.016	0.001	4.878	0.0	1	100	0.067	0.001	0.244	0.244	See	See
9	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.016	0.000	5.032	0.0	-1	100	0.070	0.003	0.227	0.227	See	See
12	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.017	0.001	4.810	0.0	1	5	0.071	0.002	0.235	0.235	See	See
15	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.017	0.001	4.846	0.0	1	1	0.069	0.002	0.238	0.238	See	See
18	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.005	0.000	0.345	0.0	-1	1	0.009	0.000	0.489	0.489	See	See
21	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.005	0.000	0.324	0.0	-1	5	0.009	0.000	0.525	0.526	See	See
24	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.005	0.000	0.336	0.0	1	100	0.010	0.000	0.475	0.475	See	See
27	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.006	0.001	0.285	0.0	-1	100	0.010	0.000	0.588	0.588	See	See
30	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.005	0.000	0.321	0.0	1	5	0.010	0.001	0.503	0.506	See	See
33	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.005	0.000	0.326	0.0	1	1	0.010	0.000	0.502	0.503	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	n_jobs	n_neighbors	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KNeighborsClassifier_brute_force	predict	100000	1000	100	2.571	0.223	0.0	0.003	-1	1	0.457	0.013	5.630	5.632	See	See
2	KNeighborsClassifier_brute_force	predict	100000	1	100	0.036	0.005	0.0	0.036	-1	1	0.015	0.001	2.505	2.507	See	See
4	KNeighborsClassifier_brute_force	predict	100000	1000	100	3.395	0.047	0.0	0.003	-1	5	0.467	0.015	7.270	7.274	See	See
5	KNeighborsClassifier_brute_force	predict	100000	1	100	0.035	0.003	0.0	0.035	-1	5	0.014	0.001	2.470	2.478	See	See
7	KNeighborsClassifier_brute_force	predict	100000	1000	100	2.683	0.028	0.0	0.003	1	100	0.514	0.008	5.216	5.217	See	See
8	KNeighborsClassifier_brute_force	predict	100000	1	100	0.031	0.002	0.0	0.031	1	100	0.014	0.000	2.206	2.206	See	See
10	KNeighborsClassifier_brute_force	predict	100000	1000	100	3.457	0.049	0.0	0.003	-1	100	0.517	0.010	6.691	6.692	See	See
11	KNeighborsClassifier_brute_force	predict	100000	1	100	0.036	0.005	0.0	0.036	-1	100	0.016	0.003	2.264	2.298	See	See
13	KNeighborsClassifier_brute_force	predict	100000	1000	100	2.688	0.028	0.0	0.003	1	5	0.461	0.007	5.824	5.825	See	See
14	KNeighborsClassifier_brute_force	predict	100000	1	100	0.033	0.003	0.0	0.033	1	5	0.014	0.000	2.314	2.315	See	See
16	KNeighborsClassifier_brute_force	predict	100000	1000	100	1.822	0.012	0.0	0.002	1	1	0.458	0.018	3.977	3.980	See	See
17	KNeighborsClassifier_brute_force	predict	100000	1	100	0.032	0.003	0.0	0.032	1	1	0.015	0.002	2.149	2.160	See	See
19	KNeighborsClassifier_brute_force	predict	100000	1000	2	2.097	0.068	0.0	0.002	-1	1	0.101	0.002	20.681	20.688	See	See
20	KNeighborsClassifier_brute_force	predict	100000	1	2	0.006	0.004	0.0	0.006	-1	1	0.001	0.000	9.212	9.321	See	See
22	KNeighborsClassifier_brute_force	predict	100000	1000	2	3.053	0.063	0.0	0.003	-1	5	0.100	0.001	30.402	30.405	See	See
23	KNeighborsClassifier_brute_force	predict	100000	1	2	0.006	0.001	0.0	0.006	-1	5	0.001	0.000	9.631	9.788	See	See
25	KNeighborsClassifier_brute_force	predict	100000	1000	2	2.405	0.021	0.0	0.002	1	100	0.161	0.005	14.900	14.906	See	See
26	KNeighborsClassifier_brute_force	predict	100000	1	2	0.004	0.001	0.0	0.004	1	100	0.001	0.000	5.036	5.110	See	See
28	KNeighborsClassifier_brute_force	predict	100000	1000	2	3.036	0.051	0.0	0.003	-1	100	0.162	0.004	18.768	18.774	See	See
29	KNeighborsClassifier_brute_force	predict	100000	1	2	0.007	0.002	0.0	0.007	-1	100	0.001	0.000	8.728	8.828	See	See
31	KNeighborsClassifier_brute_force	predict	100000	1000	2	2.387	0.024	0.0	0.002	1	5	0.103	0.003	23.073	23.082	See	See
32	KNeighborsClassifier_brute_force	predict	100000	1	2	0.003	0.000	0.0	0.003	1	5	0.001	0.000	5.158	5.220	See	See
34	KNeighborsClassifier_brute_force	predict	100000	1000	2	1.477	0.025	0.0	0.001	1	1	0.101	0.003	14.567	14.575	See	See
35	KNeighborsClassifier_brute_force	predict	100000	1	2	0.002	0.000	0.0	0.002	1	1	0.001	0.000	3.376	3.417	See	See

KNeighborsClassifier_kd_tree

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: algorithm=kd_tree.

WARNING! Mismatch between validation scores for 12 predictions (50.0%). See below.

	estimator	function	n_samples_train	n_samples	n_features	accuracy_score_sklearn	accuracy_score_sklearnex	diff_accuracy_scores
1	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	0.929	0.910	0.019
4	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	0.946	0.941	0.005
7	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	0.951	0.940	0.011
10	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	0.951	0.940	0.011
13	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	0.946	0.941	0.005
16	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	0.929	0.910	0.019
19	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.891	0.879	0.012
22	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.911	0.905	0.006
25	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.894	0.917	0.023
28	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.894	0.917	0.023
31	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.911	0.905	0.006
34	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.891	0.879	0.012

Raw results

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	n_jobs	n_neighbors	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.358	0.105	0.024	0.0	-1	1	0.948	0.091	3.543	3.559	See	See
3	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	4.787	0.058	0.017	0.0	-1	5	0.899	0.013	5.322	5.322	See	See
6	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	4.429	0.102	0.018	0.0	1	100	0.865	0.011	5.123	5.123	See	See
9	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	4.361	0.084	0.018	0.0	-1	100	0.925	0.013	4.715	4.716	See	See
12	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	4.405	0.187	0.018	0.0	1	5	0.873	0.013	5.043	5.044	See	See
15	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	4.292	0.110	0.019	0.0	1	1	0.909	0.015	4.722	4.722	See	See
18	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.001	0.015	0.0	-1	1	0.004	0.002	0.251	0.280	See	See
21	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	0.023	0.0	-1	5	0.002	0.001	0.398	0.521	See	See
24	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	0.026	0.0	1	100	0.001	0.000	0.503	0.537	See	See
27	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	0.024	0.0	-1	100	0.001	0.000	0.598	0.604	See	See
30	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	0.023	0.0	1	5	0.001	0.001	0.553	0.624	See	See
33	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	0.023	0.0	1	1	0.001	0.000	0.649	0.651	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	n_jobs	n_neighbors	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	1.022	1.203	0.000	0.001	-1	1	0.161	0.003	6.361	6.362	See	See
2	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.004	0.001	0.000	0.004	-1	1	0.000	0.000	9.294	10.592	See	See
4	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	1.345	0.448	0.000	0.001	-1	5	0.282	0.008	4.771	4.773	See	See
5	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.004	0.001	0.000	0.004	-1	5	0.000	0.000	8.135	8.677	See	See
7	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	7.718	0.702	0.000	0.008	1	100	0.893	0.025	8.646	8.649	See	See
8	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.004	0.001	0.000	0.004	1	100	0.001	0.000	3.701	3.965	See	See
10	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	3.956	0.413	0.000	0.004	-1	100	0.862	0.012	4.589	4.590	See	See
11	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.006	0.001	0.000	0.006	-1	100	0.001	0.000	6.782	7.092	See	See
13	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	2.260	0.540	0.000	0.002	1	5	0.294	0.008	7.697	7.700	See	See
14	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.002	0.001	0.000	0.002	1	5	0.001	0.000	3.282	3.524	See	See
16	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	1.286	0.378	0.000	0.001	1	1	0.153	0.002	8.416	8.417	See	See
17	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.001	0.000	0.000	0.001	1	1	0.000	0.000	3.734	3.976	See	See
19	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.035	0.019	0.000	0.000	-1	1	0.001	0.000	60.588	60.795	See	See
20	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.003	0.001	0.000	0.003	-1	1	0.000	0.000	20.093	20.386	See	See
22	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.034	0.003	0.000	0.000	-1	5	0.001	0.000	38.829	38.889	See	See
23	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.003	0.000	0.000	0.003	-1	5	0.000	0.000	18.019	18.505	See	See
25	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.046	0.011	0.000	0.000	1	100	0.006	0.000	7.886	7.891	See	See
26	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.001	0.000	0.000	0.001	1	100	0.000	0.000	5.243	5.364	See	See
28	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.054	0.010	0.000	0.000	-1	100	0.007	0.002	7.948	8.334	See	See
29	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.003	0.000	0.000	0.003	-1	100	0.000	0.000	17.677	18.098	See	See
31	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.031	0.001	0.001	0.000	1	5	0.001	0.000	35.516	35.540	See	See
32	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.001	0.000	0.000	0.001	1	5	0.000	0.000	5.273	5.349	See	See
34	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.029	0.001	0.001	0.000	1	1	0.001	0.000	50.351	50.677	See	See
35	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.001	0.000	0.000	0.001	1	1	0.000	0.000	5.708	5.808	See	See

KMeans_tall

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: algorithm=full, n_clusters=3, max_iter=30, n_init=1, tol=1e-16.

Raw results

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KMeans_tall	fit	1000000	1000000	2	0.687	0.195	30	0.023	0.0	random	0.334	0.016	2.057	2.060	See	See
3	KMeans_tall	fit	1000000	1000000	2	0.740	0.089	30	0.022	0.0	k-means++	0.379	0.007	1.950	1.951	See	See
6	KMeans_tall	fit	1000000	1000000	100	8.589	0.206	30	0.093	0.0	random	4.943	0.115	1.738	1.738	See	See
9	KMeans_tall	fit	1000000	1000000	100	9.134	0.071	30	0.088	0.0	k-means++	5.091	0.064	1.794	1.794	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KMeans_tall	predict	1000000	1000	2	0.002	0.001	30	0.007	0.000	random	0.0	0.0	7.917	11.107	See	See
2	KMeans_tall	predict	1000000	1	2	0.002	0.000	30	0.000	0.002	random	0.0	0.0	12.524	12.651	See	See
4	KMeans_tall	predict	1000000	1000	2	0.002	0.000	30	0.007	0.000	k-means++	0.0	0.0	9.498	10.353	See	See
5	KMeans_tall	predict	1000000	1	2	0.002	0.002	30	0.000	0.002	k-means++	0.0	0.0	14.241	14.569	See	See
7	KMeans_tall	predict	1000000	1000	100	0.002	0.000	30	0.354	0.000	random	0.0	0.0	7.258	7.791	See	See
8	KMeans_tall	predict	1000000	1	100	0.002	0.000	30	0.000	0.002	random	0.0	0.0	10.896	11.376	See	See
10	KMeans_tall	predict	1000000	1000	100	0.002	0.000	30	0.371	0.000	k-means++	0.0	0.0	6.804	7.406	See	See
11	KMeans_tall	predict	1000000	1	100	0.002	0.000	30	0.000	0.002	k-means++	0.0	0.0	11.447	11.568	See	See

KMeans_short

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: algorithm=full, n_clusters=300, max_iter=20, n_init=1, tol=1e-16.

WARNING! Mismatch between validation scores for 4 predictions (50.0%). See below.

	estimator	function	n_samples_train	n_samples	n_features	adjusted_rand_score_sklearn	adjusted_rand_score_sklearnex	diff_adjusted_rand_scores
1	KMeans_short	predict	10000	1000	2	0.000126	-0.000965	0.001090
4	KMeans_short	predict	10000	1000	2	0.001245	-0.000750	0.001995
7	KMeans_short	predict	10000	1000	100	0.278733	0.293767	0.015034
10	KMeans_short	predict	10000	1000	100	0.317011	0.256968	0.060044

Raw results

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KMeans_short	fit	10000	10000	2	0.109	0.004	20	0.001	0.0	random	0.059	0.004	1.866	1.871	See	See
3	KMeans_short	fit	10000	10000	2	0.331	0.007	20	0.000	0.0	k-means++	0.147	0.005	2.249	2.250	See	See
6	KMeans_short	fit	10000	10000	100	0.365	0.006	20	0.022	0.0	random	0.287	0.005	1.270	1.270	See	See
9	KMeans_short	fit	10000	10000	100	1.221	0.021	20	0.007	0.0	k-means++	0.681	0.007	1.791	1.792	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KMeans_short	predict	10000	1000	2	0.002	0.000	20	0.007	0.000	random	0.001	0.0	3.213	3.225	See	See
2	KMeans_short	predict	10000	1	2	0.002	0.000	20	0.000	0.002	random	0.000	0.0	10.902	11.014	See	See
4	KMeans_short	predict	10000	1000	2	0.002	0.000	20	0.007	0.000	k-means++	0.001	0.0	3.194	3.205	See	See
5	KMeans_short	predict	10000	1	2	0.002	0.000	20	0.000	0.002	k-means++	0.000	0.0	9.957	10.112	See	See
7	KMeans_short	predict	10000	1000	100	0.004	0.000	20	0.220	0.000	random	0.002	0.0	2.222	2.234	See	See
8	KMeans_short	predict	10000	1	100	0.002	0.000	20	0.000	0.002	random	0.000	0.0	9.202	9.390	See	See
10	KMeans_short	predict	10000	1000	100	0.004	0.001	20	0.199	0.000	k-means++	0.002	0.0	2.507	2.513	See	See
11	KMeans_short	predict	10000	1	100	0.002	0.000	20	0.000	0.002	k-means++	0.000	0.0	9.121	9.176	See	See

LogisticRegression

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: penalty=l2, dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1, class_weight=nan, random_state=nan, solver=lbfgs, max_iter=100, multi_class=auto, verbose=0, warm_start=False, n_jobs=nan, l1_ratio=nan.

WARNING! Mismatch between validation scores for 2 predictions (50.0%). See below.

	estimator	function	n_samples_train	n_samples	n_features	accuracy_score_sklearn	accuracy_score_sklearnex	diff_accuracy_scores
1	LogisticRegression	predict	1000000	1000	100	0.56	0.55	0.01
4	LogisticRegression	predict	1000	100	10000	0.35	0.28	0.07

Raw results

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	LogisticRegression	fit	1000000	1000000	100	19.512	0.381	[20]	0.041	0.000	3.787	0.104	5.152	5.154	See	See
3	LogisticRegression	fit	1000	1000	10000	1.722	0.821	[26]	0.046	0.002	1.778	0.032	0.968	0.969	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	LogisticRegression	predict	1000000	1000	100	0.001	0.001	[20]	1.325	0.0	0.001	0.002	0.559	1.200	See	See
2	LogisticRegression	predict	1000000	1	100	0.000	0.000	[20]	0.010	0.0	0.000	0.000	0.316	0.321	See	See
4	LogisticRegression	predict	1000	100	10000	0.003	0.000	[26]	2.891	0.0	0.008	0.001	0.359	0.360	See	See
5	LogisticRegression	predict	1000	1	10000	0.000	0.000	[26]	0.661	0.0	0.002	0.000	0.070	0.072	See	See

Ridge

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: alpha=1.0, fit_intercept=True, normalize=deprecated, copy_X=True, max_iter=nan, tol=0.001, solver=auto, random_state=nan.

WARNING! Mismatch between validation scores for 1 prediction (25.0%). See below.

	estimator	function	n_samples_train	n_samples	n_features	r2_score_sklearn	r2_score_sklearnex	diff_r2_scores
1	Ridge	predict	1000	1000	10000	0.082567	0.122191	0.039624

Raw results

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	Ridge	fit	1000	1000	10000	0.354	0.004	0.226	0.0	0.361	0.007	0.981	0.981	See	See
3	Ridge	fit	1000000	1000000	100	1.700	0.104	0.471	0.0	0.531	0.208	3.201	3.437	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	Ridge	predict	1000	1000	10000	0.016	0.0	4.913	0.0	0.028	0.002	0.584	0.586	See	See
2	Ridge	predict	1000	1	10000	0.000	0.0	0.867	0.0	0.000	0.000	0.552	0.611	See	See
4	Ridge	predict	1000000	1000	100	0.000	0.0	4.237	0.0	0.000	0.001	0.448	0.714	See	See
5	Ridge	predict	1000000	1	100	0.000	0.0	0.010	0.0	0.000	0.000	0.627	0.649	See	See